Water quality assessment in Choghakhor Wetland using water

Iranian Journal of Fisheries Sciences 15(1) 508- 523 2016

Water quality assessment in Choghakhor Wetland using

water quality index (WQI)

Fathi P.; Ebrahimi E. *; Mirghafarry M.; Esmaeili Ofogh A.

Received: November 2014 Accepted: August 2015

Abstract

The Choghakhor International Wetland plays an important role in preserving and

protection of part of the plant and animal species in the Iranian plateau. Since the water

of this wetland is utilized for different human purposes, complete periodic chemical and

physical quality assessment of its water seems necessary. Water quality index (WQI)

was calculated using the following eleven parameters: Nitrate, Nitrite, Ammonium,

Alkalinity, Hardness, Turbidity, Conductivity, Dissolved Oxygen, Total Dissolved

Solid, pH and Biochemical Oxygen Demand (BOD5). For this purpose, the relative

weight assigned to each parameter ranged from 1 to 4 based on the importance of the

parameter for aquatic environment and human health. The analyses of variance

(ANOVA) of data revealed significant differences between different periods of

sampling (p<0.01). Therefore we assigned the results in two categories: very poor and

inappropriate, which make it not suitable for human uses such as drinking. The most

important factor in assessment of water quality in this study was BOD5. The result of

this research demonstrated that this method can be used for assessment of water quality

in wetlands.

Keywords: Choghakhor Wetland, Chaharmohal Bakhtiari, Physicochemical factors,

Water quality index, Iran

Department of Natural Resources, Isfahan University of Technology, Isfahan 84156-83111,

Iran

* Corresponding author's Email: [email protected]

http://www.jifro.ir/admin_emailer.php?mod=send_form&slc_lang=fa&sid=1&[email protected]&a_ordnum=1219

509 Fathi et al., Water quality assessment in Choghakhor Wetland using water quality …

Introduction

The growth of population in the last few

decades in Iran have resulted in steady

demand for more food and water supply

and resources. Over exploitation of

surface and underground water

resources for the agricultural, industrial

and other purposes plus the adverse

effects of climate change have resulted

in sharp reduction of our water

resources. Therefore various national

plans and resource management

programs should be implemented in

order to revive and save our water

resources. Maintaining, protecting and

improving quantity and quality of water

resources necessitates implementation

of monitoring programs to quantify

changes and make decisions and

policies based on this information

(Odmis and Evrendilk, 2008; Qian et

al., 2007). The phrase “water quality”

has been developed to give a

comprehensive indication of suitability

of water resources for human

consumptions (Vaux, 2001). This term

is widely used in various sciences and

related cases and is considered as a

necessity to manage water resources

(Parparov et al., 2006). Water quality in

aquatic ecosystems is determined by

many physical, chemical and biological

factors (Sargaonkar and Deshpande,

2003). Therefore, particular problem in

the case of water quality monitoring is

the complexity associated with

analyzing the large number of measured

variables (Boyacioglu, 2006). The high

variability of water resources is due to

anthropogenic and natural influences

(Simeonov et al., 2002). There are a

number of methods for analysis of

water quality data which may vary

depending on the goals and information

needed, the study area, sample size and

sampling methods (Simeonov et al.,

2002; Boyacioglu, 2007a). One of the

most effective methods to assess water

quality is using appropriate indices

(Dwivedi, 2007). Indices are based on

the values of various physico-chemical

and biological parameters in a water

sample. The use of indices in

monitoring programs have been very

useful for assessment of ecosystem

health and also can be used as a

benchmark for appropriate and

successful assessment in management

strategies for improving water quality

(Rickwood and Carr, 2009). Water

quality index (WQI) can be used to

collect data on water quality parameters

at different times and places and to

translate the information into a single

value based on certain period of time

and spatial unit (Shultz, 2001). The

National Sanitation Foundation Water

Quality Index (NSF WQI) is one of the

first water quality indices (Brown et al.,

1970). Based on the results of WQI,

water can be classified for various

purposes (Brown, 1970). Pesce and

Wunderlin (2000) used water quality

indices to assess the water quality of the

Suquia River in Argentina (Pesce and

Wunderlin, 2000). Alobaidy et al.

(2010) applied WQI for water quality

assessment of Dokan Lake in Kurdistan

Region, Iraq from 1976 to 2000 period

to be compared with 2008 to 2009. The

Iranian Journal of Fisheries Sciences 15 (1) 2016 510

results revealed a decline in water

quality from good to poor (Alobaidy et

al., 2010). Nemati et al. (2009) used

water quality indices to assess the water

quality of Zayandehrud River (Nemati

Vernosfaderany et al., 2009).

Choghakhor Wetland is located in a

cold and dry region in central Iran

plateau; without any program for the

assessment of its water quality and

appropriate management (Shivandi et

al., 1999). This study is an attempt to

assess temporal and spatial changes in

Choghakhor water quality.

Materials and methods

Study area

The study area was choghakhor

Wetland with an area of about 2300

hectars. This wetland is located in

Gandoman-Boldaji plain of

Chaharmahal Bakhtiari Province.

Gandoman-Boldaji plain is located

between 31°50' to 32°00′ northern

latitudes and 51°00′ to 51°10′ eastern

longitudes (Shivandi et al., 1999). The

sampling was performed at eight stages

with a time interval of 45 days in four

seasons, from May to March 2010. Ten

sampling stations were considered with

a distance of 1km between adjacent

stations. Using topographic map and the

lattice method these locations were

determined on the map. Intersection of

grid lines were selected as sampling

stations (Fig. 1). The GPS device was

used to locate sampling stations (Tiner,

1999).

Sampling strategy and analytical

procedures

In order to analyze the chemical and

physical factors at each station,

samplers were washed 3 times with

wetland water. One liter of water was

taken from a depth of 30 cm and

transported to the laboratory at standard

conditions. Nitrate, nitrite, ammonium,

alkalinity, hardness, turbidity,

conductivity, dissolved oxygen (DO),

total dissolved solids (TDS), pH and

biochemical oxygen demand (BOD5)

were measured with 3 relications.

Mercury thermometer with an accuracy

of 0.1°C, Germany oxygen meter

(model WTW-OXI 196), Germany

Schott Geräte pH meter (model 666

221), American EC meter (model

CORNING, CIBA) and turbidity meter

(model DRT-15CE) were used for

measurement of water temperature, DO,

oxygen saturation percentage, pH, EC

and turbidity, respectively. Method of

remained Oxygen after 5 days using

oxygen meter instrument, calorimeter

method and optical spectroscopy

measurement, using spectrophotometer

device: AANALYST 700 PERKIAN

ELMER and JENWAY 6400 models

were used for measurement of BOD5,

nitrate and nitrite ions respectively

(APHA, 1992). Alkalinity was

calculated using titration and

calorimeter (APHA, 1992). Ammonium

and hardness were determined using

nesslerization, and titration with EDTA

methods, respectively (APHA, 1992).

Filtration and drying were performed in


order to measure TDS and total

suspended solids (TSS) (APHA, 1992).

Statistical analysis

The statistical analysis of data was

performed using SPSS 18.

Normalization and homogeneity of

variances of data were investigated

using Kolmogorov–Smirnov and Leven

tests. In order to evaluate differences

between sampling stations and stages,

one-way ANOVA analysis and Duncan

test were performed. To show spatial

and temporal variations of data, for

WQI and water quality variables the

linear diagrams and Box and Whisker

plot diagrams were used, respectively.

Method of the determination of WQI

WQI was determined based on

important human health parameters.

Water quality standards to protect

aquatics (Canadian Council of

Ministers of the Environment) (Lumb et

al., 2002; CCME, 2006) and also

standards recommended by the World

Health Organization (WHO) and

Drinking water standards of Iran

(WHO, 2004) were used. WQI

calculation includes the following steps

(Alobaidy et al., 2010):

Step 1:a weight (AW) from 1 to 4,

according to the suggestions of experts

in previous studies, (Pathak and

Banerjee, 1992; Pesce and Wunderlin,

2000; Abrahão et al., 2007; Boyacioglu,

2007b; Dwivedi and Pathak, 2007;

Kannel et al., 2007; Chougule et al.,

2009;; Karakaya and Evrendilek, 2009)

was assigned to each parameter. The

mean values of the weights given to

each parameter are presented in Table

1. The weight ratios of 1 and 4 were

considered as lowest and highest

correlations, respectively.

Step 2: relative weight (RW) was

calculated using equation 1

equation 1: RW = AW / ∑ AW

AW: assigned weight to each parameter

(based on Table 1). RW: relative

weight. The calculated relative weight

of each parameter is shown in Table 2.

step 3: using Equation 2 a quality rating

scale (Qi) was assigned for all

parameters, except for pH and DO

which Equation 3 was used.

Equation 2: Qi = (Ci / Si) × 100

Equation 3: Qi = (Ci – Vi / Si - Vi) × 100

Ci: the value of water quality parameter

obtained from the laboratory analysis,

Si: The value of water quality parameter

reported in world standards or standards

of Iran, Qi: the quality rating. Vi: the

ideal value of 7.0 for pH and 14.6 for

DO (Alobaidy et al., 2010).

step 4: The sub-indices (SIi) were

calculated for each parameter using

equation 4. WQI was estimated using

total SIi (Equation 5), water quality

class was determined using Table 3.

Equation 4: SIi = RW × Qi

Equation 5: WQI = ∑ SIi

Results

Fig. 2 shows WQI changes at the

studied stations. WQI was calculated

using a set of different parameters and

their importance on this index rating.

When pollution increased, the amount

of numerical index also increased.

Iranian Journal of Fisheries Sciences 15(1) 2016 512

Figure 1: Chaharmahal Bakhtiari and Choghakhor Wetland's map with indication of the study

area.

Table 1: Weight assigned to each parameter in different sources and the average proposed in this

study. References NO3

-

(mg/

L)

NO2-

(mg/L)

NH4+

(mg/

L)

Alkalinity

(mg/L)

Hardness

(mg/L)

Turbidity

(NTU)

EC

(µs)

TDS

(mg/L)

DO

(mg/L)

pH BOD5

(mg/L)

Abrahão et

al., 2007 2 2 - - 1 4 4 - 4 1 3

Boyacioglu 2007

3 - - - - - - - 4 1 2

Chougule et

al., 2009 - - - 3 2 - 4 - 4 4 4

Dwivedi and

Pathak.,

2007

- - - 1 1 2 2 2 4 4 3

Kannel et

al., 2007 2 2 3 - 1 - 1 2 4 1 3

Karakaya

and

Evrendilek,

2009

2 2 3 - 1 2 2 - 4 1 3

Pathak and

Banerjee.,

1992

- - - 1 1 2 2 - 4 4 3

Pesce and

Wunderlin,

2000

2 2 3 - 1 2 4 2 4 1 3

Proposed

mean 2.2 2 3 1.6 1.1 2.4 2.7 2 4 2.1 3


Figure 2: Changes of WQI in the studied stations and the various stages of sampling.

Table 2: Weight ratios of water quality parameters.

parameters

Water drinking

standard (WHO,

2004)

Aquatics standard

(CCME, 2006; Lumb

et al,. 2002)

Assigned

weight

Relative

weight

NO3- (mg/L) 50 13 2.2 0.084291

NO2- (mg/L) 3 0.06 2 0.076628

NH4+ (mg/L) 1.5 1.37 3 0.114943

Alkalinity (mg/L) 100 - 1.6 0.061303

Hardness (mg/L) 500 - 1.1 0.042146

Turbidity (NTU) 5 5 2.4 0.091954

EC (µs) 250 - 2.7 0.103448

TDS (mg/L) 500 500 4 0.153257

DO (mg/L) 6.5-8.5 6.5-9 2.1 0.080460

pH 5 - 3 0.114943

BOD5 (mg/L) 5 5 2 0.076628

Total 26.1 1

Table 3: Water quality classification based on the overall index score (Ramakrishnaiah et al.,

2009).

Water quality class Index values obtained

Excellent 50>

Good 50-100

Poor 100-200

Very poor 200-300

Unsuitable 300>

Iranian Journal of Fisheries Sciences 15(1) 2016 514

As we can see, WQI was almost

uniform and there was no significant

difference among different stations

(p=0.452). According to Table 3, water

quality at stations 4, 5, 7 and 9 was

slightly unsuitable and on the border

line. Other stations were in the very

poor quality class (Fig. 2).

Generally, water quality with an overall

average of 283.64 is in the very poor

category (Ramakrishnaiah et al., 2009)

and is diagnosed not proper for human

consumption such as drinking.

The change of WQI at different

stages are also shown in Fig. 2. The

highest value of this index was at stage

4 (late summer) and the lowest at stages

7 and 8 (winter), respectively. In

General, there was an upward trend

from stages 1 to 4 (spring and summer),

and a downward trend from stage 5 to 8

(fall and winter seasons). Significant

differences among different stages of

sampling (p<0.01), were also observed.

Statistical summary of water quality

data in Choghakhor Wetland is given in

the Table 4. Also the correlation

between WQI and water quality

parameters are shown in Table 5. In

order to achieve a correct view in

relation to factors that have caused

undesirable water quality, the results

are discussed as follows:

Discussion

The study area WQI quality

classification includes three classes of

poor, very poor and unsuitable

(Ramakrishnaiah et al., 2009) which

indicates this water resource is not

suitable and assured for the public

health. Among effective factors, only

Dissolved oxygen had the opposite

trend with this index. Other factors have

a direct relationship with WQI. In cold

seasons, effective parameters such as

hardness, alkalinity, turbidity and

electrical conductivity (EC) increased

but BOD5 and pH decreased in autumn

and winter. BOD5 reduction had a very

effective role and class of water quality

had changed from unsuitable to very

poor in these seasons and to poor in

stage 7 (early winter). The reduced

BOD5 was as a result of reduction in

agricultural activities and wastewater in

autumn.

Changes of water quality variable in

various stages of sampling are shown in

Fig. 3. The range of pH changes (7.44-

10.45), indicated that wetland water

was alkaline in nature. pH is one of the

most important factors in determining

water quality (Ahipathy and Puttaiah,

2006). The average value of pH being

9.12, showed incoherence pH of

wetland water with world standards for

aquatics (Lumb et al., 2002; CCME,

2006), Iran standard, Europe union

(Gray, 1996) and also values expressed

for surface water which was reported by

Li et al. (2009) within the range of 6.5

to 8.5 (WHO, 2004). The results

showed a positive correlation at the

level of 0.01 between the water quality

index (WQI) and pH. So that, an

increase in pH can lead to declining the

Water quality.


Table 4: Statistical summary of water quality data in Choghakhor Wetland.

parameters Minimum Maximum Mean Standard

deviation

Standard of

Iran

NO3- (mg/L) 0.060 0.406 0.195 ±0.077 50

NO2- (mg/L) 0.007 0.095 0.038 ±0.016 3

NH4+ (mg/L) 0.036 0.756 0.216 ±0.171 1.5

Alkalinity (mg/L) 108 200 140.718 ±20.299 -

Hardness (mg/L) 174.157 480.432 314.625 ±95.339 -

Turbidity (NTU) 10.712 70.787 26.506 ±12.389 5

EC (µs) 202 378 266.337 ±37.422 -

TDS (mg/L) 80 393 217.10 ±64.70 1500

DO (mg/L) 4.400 14.400 9,317 ±2.087 -

pH 7.440 10.450 9.123 ±0.752 6.5-9

BOD5 (mg/L) 9 280 72.587 ±51.368 -

Table 5: Pearson correlation coefficients between water quality parameters and WQI

*Correlation is significant at the 0.05 level.

**Correlation is significant at the 0.01 level.

A

WQI TDS

(mg/L)

NH4+

(mg/L) BOD5 (mg/L)

pH DO (mg/L)

EC

(µs)

Turbidit

y (NTU)

Hardness

(mg/L)

Alkalinit

y (mg/L)

NO2-

(mg/L) NO3

- (mg/L)

parameters

1 NO3-

(mg/L)

1 0.272+ NO2-

(mg/L)

1 -0.152 -0.216 Alkalinity

(mg/L)

1 0.354++ 0.254+ 0.087 Hardness

(mg/L)

1 0.432++ 0.271+ 0.162 -0.152 Turbidity

(NTU)

1 0.005 0.293++ 0.551++ -0.079 0.223+ EC (µs)

1 -0.085 0.250+ 0.054 0.072 -0.006 -0.002 DO (mg/L)

1 0.018 -

0.259+

-0.385++ -0.203 0.560++ 0.256+ 0.250+ pH

1 0.352++

0.064 0.111 -0.546++ -0.509++ -0.509++ 0.031 -0.04 BOD5

(mg/L)

1 0.172 -0.151 0.028 0.073 -0.192 -0.499++ 0.074 -0.468++

-0.095 NH4+ (mg/L)

1 -0.063 -0.315++

-0.219 -0.156 0.316++

-0.335++ 0.463++ 0.390++ -0.08 -0.095 TDS mg/L)

1 -0.388++ 0.143 0.980+

+

0.384++

0.083 0.097 0.176 -0.456++ -0.267+ 0.093 -0.028 WQI


B

C

D

E


F

G

H

I


J

K

Figure 3: Changes of water quality variable in the various stages of sampling. (A) pH, (B)

Biological oxygen demand, (C) Turbidity, (D) Dissolved oxygen, (E) Electrical

conductivity, (F) Total dissolved solid, (G) Hardness, (H) Alkalinity, (I) Nitrate, (J)

Nitrite, (K) Ammonium.

BOD5 with the average of 58.72 mg per

liter was much higher than the world

and Iran standards (WHO, 2004) and

reached to a critical state. According to

the fact that, non-polluted waters are

likely to have a BOD5 value less than 3

mg per liter, it Seems that BOD5 is the

most important and effective parameter

in calculating water quality index

(WQI) and high numerical index for

this area, indicated that it had played a

decisive role. BOD5 values can be due

to entering pollutions from human

activities including fishing, tourism

around the wetland or pollutions of

local sources (rural, agricultural, etc.)

(Kazi et al., 2009). So BOD5 level

shows possible organic pollution in this

area and careful assessment of wetland

needs to a long-term monitoring. The

highest correlation between water

quality parameters and WQI index at

the level of 1% was related to this

factor, which was reflected in its

effective role in water quality index.

Turbidity is also another important

factor in calculating the WQI index and

suitability of water quality for human

consumption and other users. In this

research turbidity had been in second

place of importance after BOD5 and it is

one of the factors that lead to worsen


the conditions. Calculated turbidity was

beyond limit and did not match with

aquatics standards (Lumb et al., 2002;

CCME, 2006), Europe union (Gray,

1996) and also world and Iran standards

(WHO 2004). Of course it is

noteworthy that lack of correlation

between this factor with WQI index

was the reason of reduction in the index

value at cold seasons despite the

increase in turbidity. In spite of the fact

that turbidity raised up, BOD5 reduction

played more effective role and it was

able to reduce WQI.

The amount of TSS in wetland water

was high, like turbidity. A significant

increase in TSS levels was observed

especially in autumn and winter. Based

on the provided standards, the

allowable amount of suspended solids,

wetland water quality was not suitable

for human consumption (drinking,

swimming, etc.), aquaculture and also

various utilizations such as agriculture

and industry (Lumb et al., 2002;

Hammer, 1986).

Dissolved oxygen, during the study

never, reached to the critical conditions

and water quality was good. As seen the

average of dissolved oxygen

concentration was equal to 9.31 mg per

liter that matched with the Canadian

aquatics standard (Lumb et al., 2002;

CCME, 2006), world standard (WHO,

2004) and was suitable for human

consumption (swimming, bathing and

drinking) and many aquatic organisms

(Hammer, 1986; Wilcock et al., 1995).

Dissolved oxygen was high in all the

stations and stages of sampling. One of

the reasons could be the presence of

aquatic plants and photosynthesis (Li et

al., 2009). These results showed a good

match with results from other studies

(Nemati Varnosfaderany et al., 2009;

Alobaidy et al., 2010).

The importance of EC was because

of the positive ions that had many

effects on the taste of water. So it has

considerable effects on the acceptability

levels of water for drinking (Pradeep,

1998; WHO, 2004). EC is an indirect

result of the amount of dissolved salts.

High EC can be caused by natural

atmospheric factors, certain

sedimentary rocks or a human source

such as industrial or sewage output

(WHO, 2004). The observed changes in

the rate of EC should be caused by

changing the concentration of dissolved

salts. About Choghakhor Wetland, that

can be affected by the input currents

,turbulences in the water and mixing of

bed sediments due to the shallow

wetland which caused by seasonal

winds. The results showed that EC

levels were somewhat higher than

reported EC by the World Health

Organization (WHO, 2004). But levels

of this factor were much lower than the

standard provided by the Europe Union

(Gray, 1996).

Generally, TDS changes were

similar to the EC. The EC increased

along with the increase in dissolved

solids (mostly salts). It’s seems that

strong wings, severe turbulences in

water and mixing of bed sediments also

can increase TDS value in fall and

winter. The calculated TDS in this


research was in accordance with the

world and Iran standards (WHO, 2004)

and was less than the limit. That was

suitable for human consumption

(swimming, bathing, tourism, drinking),

aquaculture and also agriculture and

industry (Hammer, 1986).

Water hardness is another important

parameter for waters quality used for

domestic, industrial, agricultural and

aquaculture consumptions. Results

obtained in this study have shown that

the water hardness was often higher

than the minimum reported by the

World Health Organization (200 mg per

liter) (WHO, 2004). According to

standard of Iran (500 mg per liter) the

reported hardness was suitable and less

than the standard level in Iran.

Alkalinity was higher than the

reported limits in world health

organization and Iran standards (WHO,

2004). When hardness and alkalinity

rates grew, water pollution and WQI

index also increased, but as seen, there

was a negative correlation between

these factors and WQI index which was

due to the reduction in WQI index in

cold seasons. BOD5 decline was the

reason for the WQI reduction.

The average amounts of nitrate and

nitrite in wetland water was 0.865 and

0.038 mg per liter respectively which

was the lowest amount of nitrogen

compounds in the wetland. Their values

was consistent with the world and Iran

standards (WHO, 2004), European

Union standard (Gray, 1996) and also

aquatics standards (Lumb et al., 2002;

CCME, 2006). Therefore the nitrate and

nitrite content of the wetland water was

suitable for aquaculture, drinking and

other purposes. One reason for the low

levels of nitrate and nitrite was the

vegetation because the inorganic

nitrogen compounds could be absorbed

by the plants (Li et al., 2009).

The most abundant form of nitrogen

compounds, after nitrate, was

ammonium. The average amount of

ammonium was 0.216 mg per liter. The

amount of ammonium, like other

compounds of nitrogen, was in the

range of the world and Iran standards

(WHO, 2004), the Union of Europe

(Gray, 1996) and also aquatics

standards (Lumb et al., 2002) which is

suitable for human usages.

The WQI index, has revealed that

the wetland water quality is not good

for public health and drinking purposes.

The addition of organic pollutants, as

well as the people and tourism activities

waste in the region were the most

effective factors that could lead to the

reduction of water quality. Long-term

and continuous monitoring should be

implemented in order to obtain better

results. Finally, the cooperation of

relevant authorities can lead to better

management and the health of this

ecosystem.

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Documents

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